Impact of obstructive sleep apnea on the expression of inflammatory mediators in diabetic macular edema
Bibliographic record
Abstract
PURPOSE: To determine differences in inflammatory markers expressed in diabetic macular edema (DME) patients with and without obstructive sleep apnea (OSA). METHODS: This was a prospective, cross-sectional study. Patients with treatment naive DME were enrolled in the study. They were stratified into 2 groups based on Apnea Hypopnea Index (AHI) from overnight polysomnography: OSA + (AHI ≥ 15) and OSA - (AHI<15). Multiplex immunoassay was performed for aqueous and serum cytokines including VEGF, placental growth factor (PGF), ICAM, IL2, IL3, IL6, IL8, IL10, IL17, vascular cell adhesion molecule-1 (VCAM1), monocyte attractant protein-1 (MCP1), epidermal growth factor (EGF) and platelet derived growth factor (PDGF). Statistical significance was defined as p < 0.004 using Bonferroni correction. RESULTS: 32 DME positive patients were enrolled in the study; of which 17 patients were OSA + and 15 OSA-. The OSA + cohort had significantly higher levels of serum EGF (p = 0.003), and trended towards higher levels of most serum cytokines including ICAM and IL6. OSA- cohort had significantly higher levels of aqueous IL17 compared to the OSA + cohort (2.97 ± 1.7 vs. 1.4 ± 0.46 pg/mL, p = 0.004). There were no significant differences in other aqueous cytokines. CONCLUSIONS: OSA + group trended towards higher levels of most serum inflammatory markers, suggesting a greater pro-inflammatory state. However, they did not have significantly greater level of aqueous cytokines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".